Optimisation of amorphous zinc tin oxide thin film transistors by remote-plasma reactive sputtering

The influence of the stoichiometry of amorphous zinc tin oxide (a-ZTO) thin films used as the semiconducting channel in thin film transistors (TFTs) is investigated. A-ZTO has been deposited using remote-plasma reactive sputtering from zinc:tin metal alloy targets with 10%, 33%, and 50% Sn at. %. Optimisations of thin films are performed by varying the oxygen flow, which is used as the reactive gas. The structural, optical, and electrical properties are investigated for the optimised films, which, after a post-deposition annealing at 500 °C in air, are also incorporated as the channel layer in TFTs. The optical band gap of a-ZTO films slightly increases from 3.5 to 3.8 eV with increasing tin content, with an average transmission ∼90% in the visible range. The surface roughness and crystallographic properties of the films are very similar before and after annealing. An a-ZTO TFT produced from the 10% Sn target shows a threshold voltage of 8 V, a switching ratio of 10$^8$, a sub-threshold slope of 0.55 V dec$^{-1}$, and a field effect mobility of 15 cm$^2$ V$^{-1}$ s$^{-1}$, which is a sharp increase from 0.8 cm$^2$ V$^{-1}$ s$^{-1}$ obtained in a reference ZnO TFT. For TFTs produced from the 33% Sn target, the mobility is further increased to 21 cm$^2$ V$^{-1}$ s$^{-1}$, but the sub-threshold slope is slightly deteriorated to 0.65 V dec$^{-1}$. For TFTs produced from the 50% Sn target, the devices can no longer be switched off (i.e., there is no channel depletion). The effect of tin content on the TFT electrical performance is explained in the light of preferential sputtering encountered in reactive sputtering, which resulted in films sputtered from 10% and 33% Sn to be stoichiometrically close to the common Zn$_2$SnO$_4$ and ZnSnO$_3$ phases.Engineering and Physical Sciences Research Council (Grant ID: EP/M013650/1

Approccio alla caratterizzazione di un lembo di bosco vetusto: il caso di Monte Egitto

An integrated approach to characterize an old growth forest patch; the Monte Egitto case study Old growth forests, i.e. forests which have achieved a remarkable age without or with a very limited disturbance, are nowadays subject of detailed studies in order to understand their characters and capacity of ecosystems services providing. In Sicily only few wooded areas are classified as old growth forest, following the heavy land use change toward agriculture during the centuries. This paper reports the results of a study carried out to characterize the vegetation of a little crater on the Mount Etna, where a residual open wood of Quercus congesta (an endemic oak of Southern Italy) survived the year 1651 lava flows surrounding the crater. About 35 years ago inside the crater some areas were planted with Calabrian Black Pine. As a consequence today there is a remarkable competition between trees of the two species. An integrated approach monitoring was adopted, taking into account both trees and understory (herbs, shrubs and tree regeneration) characters as well as bird fauna, in order to describe the current situation and monitor the effect of pine plantation thinning aimed at favouring oak regeneration and reducing pine-oak competition

Electronic structures of Zn1−x_{1-x}Cox_xO using photoemission and x-ray absorption spectroscopy

Electronic structures of Zn$_{1-x}$Co$_x$O have been investigated using photoemission spectroscopy (PES) and x-ray absorption spectroscopy (XAS). The Co 3d states are found to lie near the top of the O $2p$ valence band, with a peak around $\sim 3$ eV binding energy. The Co $2p$ XAS spectrum provides evidence that the Co ions in Zn$_{1-x}$Co$_{x}$O are in the divalent Co$^{2+}$ ($d^7$) states under the tetrahedral symmetry. Our finding indicates that the properly substituted Co ions for Zn sites will not produce the diluted ferromagnetic semiconductor property.Comment: 3 pages, 2 figure

Integrating microalgae production with anaerobic digestion: a biorefinery approach

This is the peer reviewed version of the following article: [Uggetti, E. , Sialve, B. , Trably, E. and Steyer, J. (2014), Integrating microalgae production with anaerobic digestion: a biorefinery approach. Biofuels, Bioprod. Bioref, 8: 516-529. doi:10.1002/bbb.1469], which has been published in final form at https://doi.org/10.1002/bbb.1469. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-ArchivingIn the energy and chemical sectors, alternative production chains should be considered in order to simultaneously reduce the dependence on oil and mitigate climate change. Biomass is probably the only viable alternative to fossil resources for production of liquid transportation fuels and chemicals since, besides fossils, it is one of the only available sources of carbon-rich material on Earth. Over recent years, interest in microalgae biomass has grown in both fundamental and applied research fields. The biorefinery concept includes different technologies able to convert biomass into added-value chemicals, products (food and feed) and biofuels (biodiesel, bioethanol, biohydrogen). As in oil refinery, a biorefinery aims at producing multiple products, maximizing the value derived from differences in biomass components, including microalgae. This paper provides an overview of the various microalgae-derived products, focusing on anaerobic digestion for conversion of microalgal biomass into methane. Special attention is paid to the range of possible inputs for anaerobic digestion (microalgal biomass and microalgal residue after lipid extraction) and the outputs resulting from the process (e.g. biogas and digestate). The strong interest in microalgae anaerobic digestion lies in its ability to mineralize microalgae containing organic nitrogen and phosphorus, resulting in a flux of ammonium and phosphate that can then be used as substrate for growing microalgae or that can be further processed to produce fertilizers. At present, anaerobic digestion outputs can provide nutrients, CO2 and water to cultivate microalgae, which in turn, are used as substrate for methane and fertilizer generation.Peer ReviewedPostprint (author's final draft

The First Very Long Baseline Interferometry Image of 44 GHz Methanol Maser with the KVN and VERA Array (KaVA)

We have carried out the first very long baseline interferometry (VLBI) imaging of 44 GHz class I methanol maser (7_{0}-6_{1}A^{+}) associated with a millimeter core MM2 in a massive star-forming region IRAS 18151-1208 with KaVA (KVN and VERA Array), which is a newly combined array of KVN (Korean VLBI Network) and VERA (VLBI Exploration of Radio Astrometry). We have succeeded in imaging compact maser features with a synthesized beam size of 2.7 milliarcseconds x 1.5 milliarcseconds (mas). These features are detected at a limited number of baselines within the length of shorter than approximately 650 km corresponding to 100 Mlambda in the uv-coverage. The central velocity and the velocity width of the 44 GHz methanol maser are consistent with those of the quiescent gas rather than the outflow traced by the SiO thermal line. The minimum component size among the maser features is ~ 5 mas x 2 mas, which corresponds to the linear size of ~ 15 AU x 6 AU assuming a distance of 3 kpc. The brightness temperatures of these features range from ~ 3.5 x 10^{8} to 1.0 x 10^{10} K, which are higher than estimated lower limit from a previous Very Large Array observation with the highest spatial resolution of ~ 50 mas. The 44 GHz class I methanol maser in IRAS 18151-1208 is found to be associated with the MM2 core, which is thought to be less evolved than another millimeter core MM1 associated with the 6.7 GHz class II methanol maser.Comment: 19 pages, 3 figure

PANDA: Expanded Width-Aware Message Passing Beyond Rewiring

Recent research in the field of graph neural network (GNN) has identified a critical issue known as "over-squashing," resulting from the bottleneck phenomenon in graph structures, which impedes the propagation of long-range information. Prior works have proposed a variety of graph rewiring concepts that aim at optimizing the spatial or spectral properties of graphs to promote the signal propagation. However, such approaches inevitably deteriorate the original graph topology, which may lead to a distortion of information flow. To address this, we introduce an expanded width-aware (PANDA) message passing, a new message passing paradigm where nodes with high centrality, a potential source of over-squashing, are selectively expanded in width to encapsulate the growing influx of signals from distant nodes. Experimental results show that our method outperforms existing rewiring methods, suggesting that selectively expanding the hidden state of nodes can be a compelling alternative to graph rewiring for addressing the over-squashing.Comment: Accepted at ICML 202

Stochastic Sampling for Contrastive Views and Hard Negative Samples in Graph-based Collaborative Filtering

Graph-based collaborative filtering (CF) has emerged as a promising approach in recommendation systems. Despite its achievements, graph-based CF models face challenges due to data sparsity and negative sampling. In this paper, we propose a novel Stochastic sampling for i) COntrastive views and ii) hard NEgative samples (SCONE) to overcome these issues. By considering that they are both sampling tasks, we generate dynamic augmented views and diverse hard negative samples via our unified stochastic sampling framework based on score-based generative models. In our comprehensive evaluations with 6 benchmark datasets, our proposed SCONE significantly improves recommendation accuracy and robustness, and demonstrates the superiority of our approach over existing CF models. Furthermore, we prove the efficacy of user-item specific stochastic sampling for addressing the user sparsity and item popularity issues. The integration of the stochastic sampling and graph-based CF obtains the state-of-the-art in personalized recommendation systems, making significant strides in information-rich environments

RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation

Contrastive learning (CL) has emerged as a promising technique for improving recommender systems, addressing the challenge of data sparsity by leveraging self-supervised signals from raw data. Integration of CL with graph convolutional network (GCN)-based collaborative filterings (CFs) has been explored in recommender systems. However, current CL-based recommendation models heavily rely on low-pass filters and graph augmentations. In this paper, we propose a novel CL method for recommender systems called the reaction-diffusion graph contrastive learning model (RDGCL). We design our own GCN for CF based on both the diffusion, i.e., low-pass filter, and the reaction, i.e., high-pass filter, equations. Our proposed CL-based training occurs between reaction and diffusion-based embeddings, so there is no need for graph augmentations. Experimental evaluation on 6 benchmark datasets demonstrates that our proposed method outperforms state-of-the-art CL-based recommendation models. By enhancing recommendation accuracy and diversity, our method brings an advancement in CL for recommender systems.Comment: Jeongwhan Choi and Hyowon Wi are co-first authors with equal contribution